Statistics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ab5ab098",
   "metadata": {},
   "source": [
    "### Phase 3 -  Statistics "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18535815",
   "metadata": {},
   "source": [
    "    To analyse the statistical aspects of datasets dublin_dataset.csv and basel_dataset.csv, the study implemented specific procedures. The researcher conducted a thorough exploration to understand the statistical characteristics of the mentioned datasets in the context of the research tasks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4f38fba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fca506c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dublin_df=pd.read_csv(\"C:/Users/bruno/Downloads/dublin_dataset.csv\", delimiter=\";\")\n",
    "basel_df=pd.read_csv(\"C:/Users/bruno/Downloads/basel_dataset.csv\", delimiter=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "744b1a5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(  Location,Year,Total Passengers\n",
       " 0             Dublin,2019,274139\n",
       " 1             Dublin,2019,275394\n",
       " 2             Dublin,2019,408199\n",
       " 3             Dublin,2019,430823,\n",
       "   Location,Year,Total Passengers\n",
       " 0             Basel,2020,2479000\n",
       " 1              Basel,2020,822000\n",
       " 2              Basel,2020,613000\n",
       " 3              Basel,2020,780000)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dublin_df.head(4),basel_df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "052a5589",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 1 columns):\n",
      " #   Column                          Non-Null Count  Dtype \n",
      "---  ------                          --------------  ----- \n",
      " 0   Location,Year,Total Passengers  614 non-null    object\n",
      "dtypes: object(1)\n",
      "memory usage: 4.9+ KB\n"
     ]
    }
   ],
   "source": [
    "dublin_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff48c73c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 1 columns):\n",
      " #   Column                          Non-Null Count  Dtype \n",
      "---  ------                          --------------  ----- \n",
      " 0   Location,Year,Total Passengers  195 non-null    object\n",
      "dtypes: object(1)\n",
      "memory usage: 1.7+ KB\n"
     ]
    }
   ],
   "source": [
    "basel_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe6544a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((614, 1), (195, 1))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dublin_df.shape, basel_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c0be823",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Location,Year,Total Passengers\n",
      "count                             614\n",
      "unique                            614\n",
      "top                Dublin,2019,274139\n",
      "freq                                1\n"
     ]
    }
   ],
   "source": [
    "# Summary statistics\n",
    "summary_stats = dublin_df.describe()\n",
    "print(summary_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e1c22d2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Location,Year,Total Passengers\n",
      "count                             195\n",
      "unique                            188\n",
      "top                Basel,2023,2245000\n",
      "freq                                2\n"
     ]
    }
   ],
   "source": [
    "# Summary statistics\n",
    "summary_stats = basel_df.describe()\n",
    "print(summary_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "60817013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Location,Year,Total Passengers    614\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count of non-null values in each column\n",
    "non_null_counts = dublin_df.count()\n",
    "print(non_null_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f940030",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Location,Year,Total Passengers    195\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count of non-null values in each column\n",
    "non_null_counts = basel_df.count()\n",
    "print(non_null_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "699461b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: []\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: []\n",
      "Location,Year,Total Passengers    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Correlation matrix\n",
    "correlation_matrix = dublin_df.corr()\n",
    "print(correlation_matrix)\n",
    "\n",
    "# Covariance matrix\n",
    "covariance_matrix = dublin_df.cov()\n",
    "print(covariance_matrix)\n",
    "\n",
    "# Check for missing values\n",
    "missing_values = dublin_df.isnull().sum()\n",
    "print(missing_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "754fd5c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: []\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: []\n",
      "Location,Year,Total Passengers    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Correlation matrix\n",
    "correlation_matrix = basel_df.corr()\n",
    "print(correlation_matrix)\n",
    "\n",
    "# Covariance matrix\n",
    "covariance_matrix = basel_df.cov()\n",
    "print(covariance_matrix)\n",
    "\n",
    "# Check for missing values\n",
    "missing_values = basel_df.isnull().sum()\n",
    "print(missing_values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "043b1302",
   "metadata": {},
   "source": [
    "### Task I - Use descriptive statistics and appropriate visualisations in order to summarise the dataset(s) used, and to help justify the chosen models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "87f98012",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Info:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 1 columns):\n",
      " #   Column                          Non-Null Count  Dtype \n",
      "---  ------                          --------------  ----- \n",
      " 0   Location,Year,Total Passengers  614 non-null    object\n",
      "dtypes: object(1)\n",
      "memory usage: 4.9+ KB\n",
      "None\n",
      "\n",
      "Descriptive Statistics:\n",
      "       Location,Year,Total Passengers\n",
      "count                             614\n",
      "unique                            614\n",
      "top                Dublin,2019,274139\n",
      "freq                                1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 250x250 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def summarize_dataset(dataframe):\n",
    "    # Display basic information about the dataset\n",
    "    print(\"Dataset Info:\")\n",
    "    print(dataframe.info())\n",
    "\n",
    "    # Display descriptive statistics\n",
    "    print(\"\\nDescriptive Statistics:\")\n",
    "    print(dataframe.describe())\n",
    "\n",
    "    # Split the 'Location,Year,Total Passengers' column\n",
    "    dataframe[['Location', 'Year', 'Total Passengers']] = dataframe['Location,Year,Total Passengers'].str.split(',', expand=True)\n",
    "\n",
    "    # Convert 'Total Passengers' to numeric (assuming it represents a numeric value)\n",
    "    dataframe['Total Passengers'] = pd.to_numeric(dataframe['Total Passengers'], errors='coerce')\n",
    "\n",
    "    # Visualize correlation matrix\n",
    "    numeric_columns = dataframe.select_dtypes(include=['float64', 'int64']).columns\n",
    "    if not numeric_columns.empty:\n",
    "        plt.figure(figsize=(5, 4))\n",
    "        sns.heatmap(dataframe[numeric_columns].corr(), annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n",
    "        plt.title(\"Correlation Matrix\")\n",
    "        plt.show()\n",
    "    else:\n",
    "        print(\"No numeric columns for correlation matrix.\")\n",
    "\n",
    "    # Visualize distribution of numerical columns\n",
    "    for column in numeric_columns:\n",
    "        plt.figure(figsize=(4, 3))\n",
    "        sns.histplot(data=dataframe, x=column, kde=True, bins=20)\n",
    "        plt.title(f\"Distribution of {column}\")\n",
    "        plt.show()\n",
    "\n",
    "    # Pairplot for selected columns\n",
    "    sns.pairplot(data=dataframe[numeric_columns])\n",
    "    plt.suptitle(\"Pairplot of Numerical Columns\", y=1.02)\n",
    "    plt.show()\n",
    "\n",
    "# Example usage with a DataFrame named dublin_df\n",
    "summarize_dataset(dublin_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3d25d56b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Info:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 1 columns):\n",
      " #   Column                          Non-Null Count  Dtype \n",
      "---  ------                          --------------  ----- \n",
      " 0   Location,Year,Total Passengers  195 non-null    object\n",
      "dtypes: object(1)\n",
      "memory usage: 1.7+ KB\n",
      "None\n",
      "\n",
      "Descriptive Statistics:\n",
      "       Location,Year,Total Passengers\n",
      "count                             195\n",
      "unique                            188\n",
      "top                Basel,2023,2245000\n",
      "freq                                2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 250x250 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "def summarize_dataset(dataframe):\n",
    "    # Display basic information about the dataset\n",
    "    print(\"Dataset Info:\")\n",
    "    print(dataframe.info())\n",
    "\n",
    "    # Display descriptive statistics\n",
    "    print(\"\\nDescriptive Statistics:\")\n",
    "    print(dataframe.describe())\n",
    "\n",
    "    # Split the 'Location,Year,Total Passengers' column\n",
    "    dataframe[['Location', 'Year', 'Total Passengers']] = dataframe['Location,Year,Total Passengers'].str.split(',', expand=True)\n",
    "\n",
    "    # Convert 'Total Passengers' to numeric (assuming it represents a numeric value)\n",
    "    dataframe['Total Passengers'] = pd.to_numeric(dataframe['Total Passengers'], errors='coerce')\n",
    "\n",
    "    # Visualize correlation matrix\n",
    "    numeric_columns = dataframe.select_dtypes(include=['float64', 'int64']).columns\n",
    "    if not numeric_columns.empty:\n",
    "        plt.figure(figsize=(5, 4))\n",
    "        sns.heatmap(dataframe[numeric_columns].corr(), annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n",
    "        plt.title(\"Correlation Matrix\")\n",
    "        plt.show()\n",
    "    else:\n",
    "        print(\"No numeric columns for correlation matrix.\")\n",
    "\n",
    "    # Visualize distribution of numerical columns\n",
    "    for column in numeric_columns:\n",
    "        plt.figure(figsize=(4, 3))\n",
    "        sns.histplot(data=dataframe, x=column, kde=True, bins=20)\n",
    "        plt.title(f\"Distribution of {column}\")\n",
    "        plt.show()\n",
    "\n",
    "    # Pairplot for selected columns\n",
    "    sns.pairplot(data=dataframe[numeric_columns])\n",
    "    plt.suptitle(\"Pairplot of Numerical Columns\", y=1.02)\n",
    "    plt.show()\n",
    "\n",
    "# Example usage with a DataFrame named basel_df\n",
    "summarize_dataset(basel_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f40f4c96",
   "metadata": {},
   "source": [
    "### Task II - Analyse the variables in your dataset(s) and use appropriate inferential statistics to gain insights on possible population values (e.g., if you were working with public transport, you could find a confidence interval for the population proportion of users commuting to Dublin by train). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "52e5e04b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confidence Interval for Population Proportion (Binary): (0.030004846050303624, 0.378539048004701)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.stats.proportion as prop\n",
    "\n",
    "def calculate_confidence_interval_binary(dataframe, target_column, threshold=0, confidence_level=0.95):\n",
    "    \"\"\"\n",
    "    Calculate the confidence interval for the population proportion for a binary column.\n",
    "\n",
    "    Parameters:\n",
    "    - dataframe: pandas DataFrame\n",
    "    - target_column: str, the column to be converted to binary\n",
    "    - threshold: float, values above this threshold are considered as 1, and below or equal as 0 (default is 0)\n",
    "    - confidence_level: float, the confidence level for the interval (default is 0.95)\n",
    "\n",
    "    Returns:\n",
    "    - confidence_interval: tuple, (lower_bound, upper_bound)\n",
    "    \"\"\"\n",
    "    # Convert the column to binary based on the threshold\n",
    "    dataframe['BinaryColumn'] = np.where(dataframe[target_column] > threshold, 1, 0)\n",
    "\n",
    "    # Count the number of occurrences in the binary column\n",
    "    successes = dataframe['BinaryColumn'].sum()\n",
    "\n",
    "    # Total number of observations\n",
    "    total_observation = len(dataframe)\n",
    "\n",
    "    # Calculate the sample proportion\n",
    "    sample_proportion = successes / total_observation\n",
    "\n",
    "    # Calculate the standard error\n",
    "    standard_error = np.sqrt((sample_proportion * (1 - sample_proportion)) / total_observation)\n",
    "\n",
    "    # Calculate the margin of error\n",
    "    margin_of_error = prop.proportion_confint(successes, total_observation, alpha=1-confidence_level)\n",
    "\n",
    "    # Calculate the confidence interval\n",
    "    confidence_interval = (sample_proportion - margin_of_error[0], sample_proportion + margin_of_error[1])\n",
    "\n",
    "    return confidence_interval\n",
    "\n",
    "# Example usage with a DataFrame named dublin_df\n",
    "confidence_interval_binary = calculate_confidence_interval_binary(dublin_df, 'Total Passengers', threshold=2000000)\n",
    "print(\"Confidence Interval for Population Proportion (Binary):\", confidence_interval_binary)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53d0b862",
   "metadata": {},
   "source": [
    "### This output consists of two values:\n",
    "\n",
    "Lower Bound: 0.030004846050303624\n",
    "Upper Bound: 0.378539048004701\n",
    "These values represent the lower and upper bounds of the confidence interval, respectively.\n",
    "\n",
    "Interpretation:\n",
    "\n",
    "With 95% confidence, we estimate that the true population proportion (binary) falls within the range of approximately 0.030 to 0.379.\n",
    "\n",
    "The confidence interval provides a level of uncertainty around the estimated proportion. In other words, if we were to repeat the process of sampling and calculating the confidence interval many times, we would expect the true population proportion to fall within this range in about 95% of those cases.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fa7aeb59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confidence Interval for Population Proportion (Binary): (0.06641451368359041, 0.7433375906066674)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.stats.proportion as prop\n",
    "\n",
    "def calculate_confidence_interval_binary(dataframe, target_column, threshold=0, confidence_level=0.95):\n",
    "    \"\"\"\n",
    "    Calculate the confidence interval for the population proportion for a binary column.\n",
    "\n",
    "    Parameters:\n",
    "    - dataframe: pandas DataFrame\n",
    "    - target_column: str, the column to be converted to binary\n",
    "    - threshold: float, values above this threshold are considered as 1, and below or equal as 0 (default is 0)\n",
    "    - confidence_level: float, the confidence level for the interval (default is 0.95)\n",
    "\n",
    "    Returns:\n",
    "    - confidence_interval: tuple, (lower_bound, upper_bound)\n",
    "    \"\"\"\n",
    "    # Convert the column to binary based on the threshold\n",
    "    dataframe['BinaryColumn'] = np.where(dataframe[target_column] > threshold, 1, 0)\n",
    "\n",
    "    # Count the number of occurrences in the binary column\n",
    "    successes = dataframe['BinaryColumn'].sum()\n",
    "\n",
    "    # Total number of observations\n",
    "    total_observation = len(dataframe)\n",
    "\n",
    "    # Calculate the sample proportion\n",
    "    sample_proportion = successes / total_observation\n",
    "\n",
    "    # Calculate the standard error\n",
    "    standard_error = np.sqrt((sample_proportion * (1 - sample_proportion)) / total_observation)\n",
    "\n",
    "    # Calculate the margin of error\n",
    "    margin_of_error = prop.proportion_confint(successes, total_observation, alpha=1-confidence_level)\n",
    "\n",
    "    # Calculate the confidence interval\n",
    "    confidence_interval = (sample_proportion - margin_of_error[0], sample_proportion + margin_of_error[1])\n",
    "\n",
    "    return confidence_interval\n",
    "\n",
    "# Example usage with a DataFrame named basel_df\n",
    "confidence_interval_binary_basel = calculate_confidence_interval_binary(basel_df, 'Total Passengers', threshold=2000000)\n",
    "print(\"Confidence Interval for Population Proportion (Binary):\", confidence_interval_binary_basel)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7250fa9e",
   "metadata": {},
   "source": [
    "#### This values represents the calculated confidence interval for the population proportion based on the binary conversion of the 'Total Passengers' column in your dataset. Let's break down the interpretation:\n",
    "\n",
    "Lower Bound: 0.06641451368359041\n",
    "Upper Bound: 0.7433375906066674\n",
    "This means:\n",
    "\n",
    "With 95% confidence, we estimate that the true population proportion (binary) falls within the range of approximately 0.066 to 0.743.\n",
    "\n",
    "The confidence interval provides a level of uncertainty around the estimated proportion. In other words, if we were to repeat the process of sampling and calculating the confidence interval many times, we would expect the true population proportion to fall within this range in about 95% of those cases.\n",
    "\n",
    "The specific interpretation of the binary variable depends on the context of your analysis. In general terms, it suggests the range of possible values for the proportion of occurrences of an event (coded as 1) in your dataset, after applying a threshold to convert the 'Total Passengers' column to binary values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a761bc9c",
   "metadata": {},
   "source": [
    "### Task III - Undertake research to find similarities between some country(s) against Ireland and apply parametric and non-parametric inferential statistical techniques to compare them (e.g., t-test, analysis of variance, Wilcoxon test, chi-squared test, among others). You must justify your choices and verify the applicability of the tests. Hypotheses and conclusions must be clearly stated. You are expected to use at least 5 different inferential statistics tests. [0-40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "83451162",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-Statistic: 1.0823388845216377\n",
      "P-Value: 0.2795007966457365\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "def t_test_total_passengers(dublin_df, basel_df):\n",
    "    \"\"\"\n",
    "    Perform an independent samples t-test for the 'Total Passengers' between Dublin and Basel.\n",
    "\n",
    "    Parameters:\n",
    "    - dublin_df: pandas DataFrame for Dublin\n",
    "    - basel_df: pandas DataFrame for Basel\n",
    "\n",
    "    Returns:\n",
    "    - t_statistic: The t-statistic from the t-test\n",
    "    - p_value: The p-value associated with the t-statistic\n",
    "    \"\"\"\n",
    "    # Extract the 'Total Passengers' column from each dataset\n",
    "    dublin_passengers = dublin_df['Total Passengers']\n",
    "    basel_passengers = basel_df['Total Passengers']\n",
    "\n",
    "    # Perform independent samples t-test\n",
    "    t_statistic, p_value = stats.ttest_ind(dublin_passengers, basel_passengers, equal_var=False)\n",
    "\n",
    "    return t_statistic, p_value\n",
    "\n",
    "# Example usage with dublin_df and basel_df\n",
    "t_stat, p_val = t_test_total_passengers(dublin_df, basel_df)\n",
    "\n",
    "# Print the results\n",
    "print(\"T-Statistic:\", t_stat)\n",
    "print(\"P-Value:\", p_val)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "447399c0",
   "metadata": {},
   "source": [
    "### Task IV - Use the outcome of your analysis to deepen your research. Indicate the challenges you faced in the process.\n",
    "\n",
    "In the \"Statistics.ipynb\" Jupyter notebook, we explored datasets dublin_dataset.csv and basel_dataset.csv. We faced challenges due to limited data but collaborated to overcome them. Using specific functions like \"calculate_confidence_interval_binary\" and \"t_test_total_passengers,\" we gained insights. For instance, the confidence interval helped estimate the proportion of 'Total Passengers' above 2,000,000. The t-test compared passenger numbers between Dublin and Basel, concluding no significant difference. This statistical journey showcases our adaptability, collaboration, and determination to make sense of data complexities, highlighting the value of statistical tools in drawing meaningful conclusions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "082866a4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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